Random numbers and their generation play a key role in many modern computing systems and networks. For example, in cryptographic applications for secure networking environments, unique keys are generated and used to provide non-repeatable security authentication codes. It is common for these unique keys to be defined as random numbers having been generated by a random number generation device or method. One type of random number generation method produces pseudo-random numbers or deterministic random numbers. The pseudo-random number generation method involves repeated evaluation of a mathematical formula to generate random numbers. The random numbers generated by evaluation of the mathematical formula are characterized as being pseudo-random because the random numbers generated by successive evaluation of the mathematical formula will eventually repeat in a sequential manner. Therefore, with knowledge of the mathematical formula and appropriate input values, it is possible for the pseudo-random number generation sequence to be reproduced, thus leaving a security weakness that is susceptible to exploitation by a harmful entity.
In view of the foregoing, it is more desirable in certain applications to generate truly random numbers that are based on a source of randomness that is truly random, as opposed to a deterministic mathematical formula. Consequently, a need continually persists for improvements in technology associated with true random number generation.